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!4251 adding type support for gpu kernels for EfficientNet 

Merge pull request !4251 from Peilin/efficientnet
This commit is contained in:
mindspore-ci-bot 2020-08-14 10:25:42 +08:00 committed by Gitee
parent a23dd7147a 571094f473
commit c7b50bcdd2
5 changed files with 64 additions and 19 deletions
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2
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/transpose_gpu_kernel.cc
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@ -21,5 +21,7 @@ MS_REG_GPU_KERNEL_ONE(Transpose, KernelAttr().AddInputAttr(kNumberTypeFloat32).A
TransposeGpuFwdKernel, float)
MS_REG_GPU_KERNEL_ONE(Transpose, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
TransposeGpuFwdKernel, half)
MS_REG_GPU_KERNEL_ONE(Transpose, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
TransposeGpuFwdKernel, int)
} // namespace kernel
} // namespace mindspore

16
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/broadcast_grad_impl.cu
View File

@ -15,6 +15,7 @@
*/
#include "backend/kernel_compiler/gpu/cuda_impl/broadcast_grad_impl.cuh"
#include "backend/kernel_compiler/gpu/cuda_impl/util.cuh"
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
@ -22,9 +23,9 @@ struct MinimumGradFunc {
__device__ __forceinline__ void operator()(const T &x1, const T &x2, const bool &grad_x1, const bool &grad_x2,
const T &dy, T *dx1, T *dx2) {
if (grad_x1 && x1 < x2) {
atomicAdd(dx1, dy);
ms_atomic_add(dx1, dy);
} else if (grad_x2 && x1 >= x2) {
atomicAdd(dx2, dy);
ms_atomic_add(dx2, dy);
}
}
};
@ -34,9 +35,9 @@ struct MaximumGradFunc {
__device__ __forceinline__ void operator()(const T &x1, const T &x2, const bool &grad_x1, const bool &grad_x2,
const T &dy, T *dx1, T *dx2) {
if (grad_x1 && x1 > x2) {
atomicAdd(dx1, dy);
ms_atomic_add(dx1, dy);
} else if (grad_x2 && x1 <= x2) {
atomicAdd(dx2, dy);
ms_atomic_add(dx2, dy);
}
}
};
@ -117,6 +118,9 @@ template void NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &
cudaStream_t stream);
template void NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op,
const int *x1, const int *x2, const int *dy, int *dx1, int *dx2, cudaStream_t stream);
template void NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op,
const half *x1, const half *x2, const half *dy, half *dx1, half *dx2,
cudaStream_t stream);
template void BroadcastGrad(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const float *x1,
@ -125,3 +129,7 @@ template void BroadcastGrad(const int &l0, const int &l1, const int &l2, const i
const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const int *x1,
const int *x2, const int *dy, int *dx1, int *dx2, cudaStream_t stream);
template void BroadcastGrad(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const half *x1,
const half *x2, const half *dy, half *dx1, half *dx2, cudaStream_t stream);

6
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/transpose_impl.cu
View File

@ -1,5 +1,5 @@
/**
* Copyright 2019 Huawei Technologies Co., Ltd
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
@ -15,8 +15,10 @@
*/
#include <cuda_runtime.h>
#include "transpose_impl.cuh"
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
__global__ void Transpose(const int size, const T* input, const int* input_shape, const int* input_axis,
const int shape_size, T* output) {
@ -63,3 +65,5 @@ template void CalTranspose<float>(const int size, const float* input, const int*
const int shape_size, float* output, cudaStream_t cuda_stream);
template void CalTranspose<half>(const int size, const half* input, const int* input_shape, const int* input_axis,
const int shape_size, half* output, cudaStream_t cuda_stream);
template void CalTranspose<int>(const int size, const int* input, const int* input_shape, const int* input_axis,
const int shape_size, int* output, cudaStream_t cuda_stream);

16
mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_grad_gpu_kernel.cc
View File

@ -34,6 +34,22 @@ MS_REG_GPU_KERNEL_ONE(MaximumGrad,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
BroadcastOpGradGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
BroadcastOpGradGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(MaximumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
BroadcastOpGradGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)

43
tests/st/ops/gpu/test_transpose_op.py
View File

@ -28,25 +28,25 @@ context.set_context(device_target='GPU')
class Transpose(nn.Cell):
def __init__(self):
def __init__(self, nptype):
super(Transpose, self).__init__()
self.transpose = P.Transpose()
self.x_2D = Parameter(initializer(Tensor(np.arange(5 * 6).reshape(5, 6).astype(np.float32)), [5, 6]),
self.x_2D = Parameter(initializer(Tensor(np.arange(5 * 6).reshape(5, 6).astype(nptype)), [5, 6]),
name='x_2D')
self.perm_2D = (1, 0)
self.x_3D = Parameter(initializer(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(np.float32)), [2, 2, 4]),
self.x_3D = Parameter(initializer(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(nptype)), [2, 2, 4]),
name='x_3D')
self.perm_3D = (1, 0, 2)
self.x_4D = Parameter(
initializer(Tensor(np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5).astype(np.float32)), [2, 3, 4, 5]),
initializer(Tensor(np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5).astype(nptype)), [2, 3, 4, 5]),
name='x_4D')
self.perm_4D = (0, 1, 2, 3)
self.x_5D = Parameter(
initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(np.float32)),
initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(nptype)),
[1, 2, 3, 4, 5]), name='x_5D')
self.perm_5D = (1, 0, 3, 4, 2)
@ -56,11 +56,8 @@ class Transpose(nn.Cell):
self.transpose(self.x_4D, self.perm_4D), self.transpose(self.x_5D, self.perm_5D))
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_transpose():
transpose = Transpose()
def transpose1(nptype):
transpose = Transpose(nptype)
output = transpose()
expect0 = np.array([[[0, 6, 12, 18, 24],
@ -68,11 +65,11 @@ def test_transpose():
[2, 8, 14, 20, 26],
[3, 9, 15, 21, 27],
[4, 10, 16, 22, 28],
[5, 11, 17, 23, 29]]]).astype(np.float32)
[5, 11, 17, 23, 29]]]).astype(nptype)
expect1 = np.array([[[[0, 1, 2, 3],
[8, 9, 10, 11]],
[[4, 5, 6, 7],
[12, 13, 14, 15]]]]).astype(np.float32)
[12, 13, 14, 15]]]]).astype(nptype)
expect2 = np.array([[[[[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
@ -97,7 +94,7 @@ def test_transpose():
[[100, 101, 102, 103, 104],
[105, 106, 107, 108, 109],
[110, 111, 112, 113, 114],
[115, 116, 117, 118, 119]]]]]).astype(np.float32)
[115, 116, 117, 118, 119]]]]]).astype(nptype)
expect3 = np.array([[[[[[0, 20, 40],
[1, 21, 41],
[2, 22, 42],
@ -138,8 +135,26 @@ def test_transpose():
[76, 96, 116],
[77, 97, 117],
[78, 98, 118],
[79, 99, 119]]]]]]).astype(np.float32)
[79, 99, 119]]]]]]).astype(nptype)
assert (output[0].asnumpy() == expect0).all()
assert (output[1].asnumpy() == expect1).all()
assert (output[2].asnumpy() == expect2).all()
assert (output[3].asnumpy() == expect3).all()
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_transpose_float32():
transpose1(np.float32)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_transpose_float16():
transpose1(np.float16)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_transpose_int32():
transpose1(np.int32)